Source code for pyro.poutine.scale_messenger

# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0

import torch

from pyro.poutine.util import is_validation_enabled

from .messenger import Messenger

[docs]class ScaleMessenger(Messenger): """ Given a stochastic function with some sample statements and a positive scale factor, scale the score of all sample and observe sites in the function. Consider the following Pyro program: >>> def model(x): ... s = pyro.param("s", torch.tensor(0.5)) ... pyro.sample("z", dist.Normal(x, s), obs=torch.tensor(1.0)) ``scale`` multiplicatively scales the log-probabilities of sample sites: >>> scaled_model = pyro.poutine.scale(model, scale=0.5) >>> scaled_tr = pyro.poutine.trace(scaled_model).get_trace(0.0) >>> unscaled_tr = pyro.poutine.trace(model).get_trace(0.0) >>> bool((scaled_tr.log_prob_sum() == 0.5 * unscaled_tr.log_prob_sum()).all()) True :param fn: a stochastic function (callable containing Pyro primitive calls) :param scale: a positive scaling factor :returns: stochastic function decorated with a :class:`~pyro.poutine.scale_messenger.ScaleMessenger` """ def __init__(self, scale): if isinstance(scale, torch.Tensor): if is_validation_enabled() and not (scale > 0).all(): raise ValueError("Expected scale > 0 but got {}. ".format(scale) + "Consider using poutine.mask() instead of poutine.scale().") elif not (scale > 0): raise ValueError("Expected scale > 0 but got {}".format(scale)) super().__init__() self.scale = scale def _process_message(self, msg): msg["scale"] = self.scale * msg["scale"] return None